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Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients

PURPOSE: We prospectively evaluated whether a strategy using point spread function (PSF) reconstruction for both diagnostic and quantitative analysis in non-small cell lung cancer (NSCLC) patients meets the European Association of Nuclear Medicine (EANM) guidelines for harmonization of quantitative...

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Autores principales: Lasnon, Charline, Desmonts, Cédric, Quak, Elske, Gervais, Radj, Do, Pascal, Dubos-Arvis, Catherine, Aide, Nicolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer-Verlag 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3679414/
https://www.ncbi.nlm.nih.gov/pubmed/23564036
http://dx.doi.org/10.1007/s00259-013-2391-1
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author Lasnon, Charline
Desmonts, Cédric
Quak, Elske
Gervais, Radj
Do, Pascal
Dubos-Arvis, Catherine
Aide, Nicolas
author_facet Lasnon, Charline
Desmonts, Cédric
Quak, Elske
Gervais, Radj
Do, Pascal
Dubos-Arvis, Catherine
Aide, Nicolas
author_sort Lasnon, Charline
collection PubMed
description PURPOSE: We prospectively evaluated whether a strategy using point spread function (PSF) reconstruction for both diagnostic and quantitative analysis in non-small cell lung cancer (NSCLC) patients meets the European Association of Nuclear Medicine (EANM) guidelines for harmonization of quantitative values. METHODS: The NEMA NU-2 phantom was used to determine the optimal filter to apply to PSF-reconstructed images in order to obtain recovery coefficients (RCs) fulfilling the EANM guidelines for tumour positron emission tomography (PET) imaging (PSF(EANM)). PET data of 52 consecutive NSCLC patients were reconstructed with unfiltered PSF reconstruction (PSF(allpass)), PSF(EANM) and with a conventional ordered subset expectation maximization (OSEM) algorithm known to meet EANM guidelines. To mimic a situation in which a patient would undergo pre- and post-therapy PET scans on different generation PET systems, standardized uptake values (SUVs) for OSEM reconstruction were compared to SUVs for PSF(EANM) and PSF(allpass) reconstruction. RESULTS: Overall, in 195 lesions, Bland-Altman analysis demonstrated that the mean ratio between PSF(EANM) and OSEM data was 1.03 [95 % confidence interval (CI) 0.94–1.12] and 1.02 (95 % CI 0.90–1.14) for SUV(max) and SUV(mean), respectively. No difference was noticed when analysing lesions based on their size and location or on patient body habitus and image noise. Ten patients (84 lesions) underwent two PET scans for response monitoring. Using the European Organization for Research and Treatment of Cancer (EORTC) criteria, there was an almost perfect agreement between OSEM(PET1)/OSEM(PET2) (current standard) and OSEM(PET1)/PSF(EANM-PET2) or PSF(EANM-PET1)/OSEM(PET2) with kappa values of 0.95 (95 % CI 0.91–1.00) and 0.99 (95 % CI 0.96–1.00), respectively. The use of PSF(allpass) either for pre- or post-treatment (i.e. OSEM(PET1)/PSF(allpass-PET2) or PSF(allpass-PET1)/OSEM(PET2)) showed considerably less agreement with kappa values of 0.75 (95 % CI 0.67–0.83) and 0.86 (95 % CI 0.78–0.94), respectively. CONCLUSION: Protocol-optimized images and compliance with EANM guidelines allowed for a reliable pre- and post-therapy evaluation when using different generation PET systems. These data obtained in NSCLC patients could be extrapolated to other solid tumours. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00259-013-2391-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-36794142013-06-14 Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients Lasnon, Charline Desmonts, Cédric Quak, Elske Gervais, Radj Do, Pascal Dubos-Arvis, Catherine Aide, Nicolas Eur J Nucl Med Mol Imaging Original Article PURPOSE: We prospectively evaluated whether a strategy using point spread function (PSF) reconstruction for both diagnostic and quantitative analysis in non-small cell lung cancer (NSCLC) patients meets the European Association of Nuclear Medicine (EANM) guidelines for harmonization of quantitative values. METHODS: The NEMA NU-2 phantom was used to determine the optimal filter to apply to PSF-reconstructed images in order to obtain recovery coefficients (RCs) fulfilling the EANM guidelines for tumour positron emission tomography (PET) imaging (PSF(EANM)). PET data of 52 consecutive NSCLC patients were reconstructed with unfiltered PSF reconstruction (PSF(allpass)), PSF(EANM) and with a conventional ordered subset expectation maximization (OSEM) algorithm known to meet EANM guidelines. To mimic a situation in which a patient would undergo pre- and post-therapy PET scans on different generation PET systems, standardized uptake values (SUVs) for OSEM reconstruction were compared to SUVs for PSF(EANM) and PSF(allpass) reconstruction. RESULTS: Overall, in 195 lesions, Bland-Altman analysis demonstrated that the mean ratio between PSF(EANM) and OSEM data was 1.03 [95 % confidence interval (CI) 0.94–1.12] and 1.02 (95 % CI 0.90–1.14) for SUV(max) and SUV(mean), respectively. No difference was noticed when analysing lesions based on their size and location or on patient body habitus and image noise. Ten patients (84 lesions) underwent two PET scans for response monitoring. Using the European Organization for Research and Treatment of Cancer (EORTC) criteria, there was an almost perfect agreement between OSEM(PET1)/OSEM(PET2) (current standard) and OSEM(PET1)/PSF(EANM-PET2) or PSF(EANM-PET1)/OSEM(PET2) with kappa values of 0.95 (95 % CI 0.91–1.00) and 0.99 (95 % CI 0.96–1.00), respectively. The use of PSF(allpass) either for pre- or post-treatment (i.e. OSEM(PET1)/PSF(allpass-PET2) or PSF(allpass-PET1)/OSEM(PET2)) showed considerably less agreement with kappa values of 0.75 (95 % CI 0.67–0.83) and 0.86 (95 % CI 0.78–0.94), respectively. CONCLUSION: Protocol-optimized images and compliance with EANM guidelines allowed for a reliable pre- and post-therapy evaluation when using different generation PET systems. These data obtained in NSCLC patients could be extrapolated to other solid tumours. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00259-013-2391-1) contains supplementary material, which is available to authorized users. Springer-Verlag 2013-04-06 2013 /pmc/articles/PMC3679414/ /pubmed/23564036 http://dx.doi.org/10.1007/s00259-013-2391-1 Text en © The Author(s) 2013 https://creativecommons.org/licenses/by-nc/2.0/ Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Original Article
Lasnon, Charline
Desmonts, Cédric
Quak, Elske
Gervais, Radj
Do, Pascal
Dubos-Arvis, Catherine
Aide, Nicolas
Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients
title Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients
title_full Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients
title_fullStr Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients
title_full_unstemmed Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients
title_short Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients
title_sort harmonizing suvs in multicentre trials when using different generation pet systems: prospective validation in non-small cell lung cancer patients
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3679414/
https://www.ncbi.nlm.nih.gov/pubmed/23564036
http://dx.doi.org/10.1007/s00259-013-2391-1
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